Water conservancy and transportation infrastructure are traditionally monitored using techniques including total stations, sensor networks, or global navigation satellite systems (GNSS). Although these techniques offer high accuracy at discrete points, they are unable to identify deformations outside of monitored locations, and increasing point density significantly increases costs. Growing interest has been shown in the recently developed unmanned aerial vehicle (UAV)–based remote sensing; nonetheless, most studies rely on single data sources, which results in poor accuracy and low robustness in complex situations. To overcome these drawbacks, this study proposes a deformation monitoring method based on UAV multisource data fusion. A high-precision fused model is created by combining high-resolution oblique imagery with airborne LiDAR point clouds. During aerial triangulation, preprocessed LiDAR point clouds serve as geometric constraints. This combines extensive texture information with high geometric accuracy to enable accurate registration within a single coordinate system. A two-stage registration strategy, comprising feature-based singular value decomposition (SVD) and the iterative closest point (ICP) algorithm, is employed for multi-period models. Surface displacements are measured using cloud-to-cloud (C2C) distances, which are then broken down into orthogonal X-, Y-, and Z-components to meet technical requirements. Validation on a reservoir dam shows that the proposed strategy greatly improves monitoring accuracy and reliability when compared to single-source approaches. The automated workflow provides a practical solution for long-term infrastructure monitoring by facilitating efficient data processing and the detection of subtle surface deformations.